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TitreRapid Risk Evaluation (ER2) using MS Excel spreadsheet: a case study of Fredericton (New Brunswick, Canada)
AuteurMcGrath, H; Stefanakis, E; Nastev, M
SourceXXIII ISPRS Congress, Commission VIII; ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. III-8, 2016 p. 27-34, https://doi.org/10.5194/isprs-annals-III-8-27-2016
Année2016
Séries alt.Ressources naturelles Canada, Contribution externe 20182519
ÉditeurCopernicus GmbH
RéunionXXIII ISPRS Congress, Commission VIII; Prague; CZ; 12-19 juillet 2016
Documentpublication en série
Lang.anglais
DOIhttps://doi.org/10.5194/isprs-annals-III-8-27-2016
Mediapapier; en ligne; numérique
ProvinceNouveau-Brunswick
ProgrammeÉvaluation rapide des risques, Géoscience pour la sécurité publique
Diffusé2016 06 07
Résumé(disponible en anglais seulement)
Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools and guidance to adequately undertake flood hazard risk assessment in order to estimate respective damages and social and economic losses. While many complex computer models have been developed for flood risk assessment, they require highly trained personnel to prepare the necessary input (hazard, inventory of the built environment, and vulnerabilities) and analyze model outputs. As such, tools which utilize open-source software or are built within popular desktop software programs are appealing alternatives. The recently developed Rapid Risk Evaluation (ER2) application runs scenario based loss assessment analyses in a Microsoft Excel spreadsheet. User input is limited to a handful of intuitive drop-down menus utilized to describe the building type, age, occupancy and the expected water level. In anticipation of local depth damage curves and other needed vulnerability parameters, those from the U.S. FEMA's Hazus-Flood software have been imported and temporarily accessed in conjunction with user input to display exposure and estimated economic losses related to the structure and the content of the building. Building types and occupancies representative of those most exposed to flooding in Fredericton (New Brunswick) were introduced and test flood scenarios were run. The algorithm was successfully validated against results from the Hazus-Flood model for the same building types and flood depths.
GEOSCAN ID311113